This study analyzes the drivers of local income inequality in Spain. It derives a novel data set of inequality metrics for a sample of municipalities over the period 2000-06. Spatial Bayesian model selection and model-averaging techniques are used in order to examine the empirical relevance of (1) spatial functional forms, (2) spatial weight matrices and (3) a large set of factors that could affect inequality. The findings suggest that local inequality is mainly explained by human capital, economic factors and local politics. In addition, the use of Bayesian geographically weighted regressions provides evidence in favour of spatially heterogeneous effects.
The drivers of local income inequality: a spatial Bayesian model-averaging approach
Vicente Rios
2019-01-01
Abstract
This study analyzes the drivers of local income inequality in Spain. It derives a novel data set of inequality metrics for a sample of municipalities over the period 2000-06. Spatial Bayesian model selection and model-averaging techniques are used in order to examine the empirical relevance of (1) spatial functional forms, (2) spatial weight matrices and (3) a large set of factors that could affect inequality. The findings suggest that local inequality is mainly explained by human capital, economic factors and local politics. In addition, the use of Bayesian geographically weighted regressions provides evidence in favour of spatially heterogeneous effects.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
